The low‐latitude mesosphere and lower thermosphere (MLT) regions are distinct and, highly turbulent transition zones in Earth's atmosphere. The scarcity of reliable measurements makes continuous monitoring of these areas challenging. Therefore, the necessity for studies focused on the MLT region cannot be overstated, as they are essential for developing effective models that meet the accuracy requirements of satellite‐based observations. The neural networks NN‐MLT model, developed using 15 years of Thermosphere, Ionosphere, and Mesosphere Energetics and Dynamics/satellite, equipped with Broadband Emission Radiometry (SABER) observed temperature data spanning from January 2006 to December 2020, employs neural network techniques. The data set was split, with 90% used for training and the remaining 10% allocated for prediction. The model's validation was tested with two other partitions (80(20) and 70(30)). The 90(10) partition, exhibiting a high correlation coefficient (R), low standard deviation (σ), and low root mean square error (RMSE), demonstrated the model's good performance. As clearly shown from statistical metrics (R, RMSE, mean, and σ) at three specific altitude levels (60, 75, and 90 km), the NN‐MLT model's performance aligns closely with the empirical model (NRLMSISE2‐0) and SABER observations. The NN‐MLT model displays a high R (0.74) and low RMSE (4.35 K) at 60 km, indicating its effective performance compared to the other two heights of 75 and 90 km. The NN‐MLT model's spatiotemporal variability in MLT temperature prediction agrees well with the SABER data at all altitudes, particularly at 60 km. While the NN‐MLT model accurately captures the seasonal variations of MLT temperature, the analysis leads to the conclusion that it consistently outperforms the empirical model and aligns closely with observations.

Drought is an extreme event that causes great economic and environmental damage. The main objective of this study is to evaluate sensitivity, characterization and propagation of drought in the Upper Blue Nile. Drought indices: standardized precipitation index (SPI) and the recently developed standardized reconnaissance drought index (RDIst) are applied for five weather stations from 1980 to 2015 to evaluate RDIst applicability in the Upper Blue Nile. From our analysis both SPI and RDIst applied for 3-, 6-, 12 month of time scales follow the same trend, but in some time steps the RDIst varies with smaller amplitude than SPI. The severity and longer duration of drought compared with others periods of meteorological drought is found in the years 1984, 2002, 2009, 2015 including five weather stations and entire Upper Blue Nile. For drought relationships the correlation analysis is made across the time scales to evaluate the relationship between meteorological drought (SPI), soil moisture drought (SMI), and hydrological drought (SRI). We found that the correlation between three indices (SPI, SMI and SRI) at different time scales the 24-month time scale is dominant and are given by 0.82, 0.63 and 0.56.

In this study, ground-level ozone is modeled using Seasonal Autoregressive Integrated Moving Average (SARIMA) and additive Holt–Winters models over the North-Western cluster of Ethiopia using four homogeneous series of more than 13 years of data from the European Center for Medium-Range Weather Forecasts. We split the dataset into training and testing sets. We used the data during the period 2007–2019 for model formulation and parameter estimation and the one year data in 2020 to test model forecasts. More than 60 SARIMA models have been generated for the time series. The goodness of fit of these models has been assessed using the Akaike information criterion and Bayesian information criterion. After rigorous assessment, the seasonal ARIMA(2,0,4)(1,1,2)[12], ARIMA(3,1,0)(2,0,0)[12], ARIMA(0,1,2)(0,0,2)[12], and ARIMA(2,0,0)(2,1,0)[12] models have been identified as best predictive models for Addis Ababa, Ras Dashen, Danakil Depression, and Bahir Dar, respectively. We applied model evaluation metrics, such as root mean square error, mean absolute error, and Mean Absolute Percentage Error (MAPE) to compare the accuracy between seasonal ARIMA and Holt–Winters models. Among the SARIMA and Holt–Winters models, our findings show that the best model for forecasting surface-level ozone is ARIMA(2,0,4)(1,1,2)[12] and ARIMA(3,1,0)(2,0,0)[12] for Addis Ababa and Ras Dashen stations, respectively. However, for Danakil Depression and Bahir Dar stations, the Holt–Winters model with α = 0.346, β = 0.023, γ = 0.36 and α = 0.302, β = 0.019, γ = 0.266, respectively, are found to be best models than the SARIMA. Moreover, the maximum MAPE were found to be less than 7.86% in the study, and hence all the forecasts are acceptable.